城市轨道交通个体轨迹的时空信息对于运营策略调整具有重要意义,个性化推荐,和应急指挥决策。然而,由于缺乏旅程观察,仅基于AFC和AVL数据很难从轨迹中准确推断未知信息。为了解决这个问题,本文提出了一种基于自适应期望最大化注意力的时空概率图模型(STPGM-AEMA),以实现个体轨迹的重构。该方法包括三个步骤:首先,通过数据挖掘和组合枚举获得个体的潜在列车备选集和出口时间备选集。然后,引入全局和局部潜在变量来构建时空概率图模型,提供未知事件的推理过程,以及关于个体轨迹的状态信息。Further,考虑到数据缺失的影响,提出了一种注意力机制增强的期望最大化算法来实现个体轨迹的最大似然估计。最后,典型的起点-目的地对数据集和实际个体轨迹跟踪数据用于验证所提出的方法的有效性。结果表明,STPGM-AEMA方法在恢复观测数据中缺失信息的准确率超过95%,这比传统方法至少准确15%(即,PTAM-MLE和MPTAM-EM)。
Spatiotemporal information on individual trajectories in urban rail transit is important for operational strategy adjustment, personalized recommendation, and emergency command decision-making. However, due to the lack of journey observations, it is difficult to accurately infer unknown information from trajectories based only on AFC and AVL data. To address the problem, this paper proposes a spatiotemporal probabilistic graphical model based on adaptive expectation maximization attention (STPGM-AEMA) to achieve the reconstruction of individual trajectories. The approach consists of three steps: first, the potential train alternative set and the egress time alternative set of individuals are obtained through data mining and combinatorial enumeration. Then, global and local potential variables are introduced to construct a spatiotemporal probabilistic graphical model, provide the inference process for unknown events, and state information about individual trajectories. Further, considering the effect of missing data, an attention mechanism-enhanced expectation-maximization algorithm is proposed to achieve maximum likelihood estimation of individual trajectories. Finally, typical datasets of origin-destination pairs and actual individual trajectory tracking data are used to validate the effectiveness of the proposed method. The results show that the STPGM-AEMA method is more than 95% accurate in recovering missing information in the observed data, which is at least 15% more accurate than the traditional methods (i.e., PTAM-MLE and MPTAM-EM).